RowMeans of DataFrame Excluding Some Columns

Introduction

In this article, we will explore how to calculate the row means of a dataframe excluding certain columns. We will cover different approaches using both base R and dplyr libraries.

The Problem

Given a dataframe with multiple columns, we want to exclude specific columns from calculating the row mean. This can be achieved by splitting the dataframe into separate dataframes based on the column names that do not match the excluded group name.

Using Base R

One way to solve this problem is by using base R functions like sapply, split, and rowMeans. Here’s an example:

# Load necessary libraries
library(dplyr)

# Create a sample dataframe
df1 <- structure(list(Leaf1 = c(1L, 46L, 100L), Leaf2 = c(2L, 22L, 22L),
                      Leaf3 = c(3L, 33L, 2L), Root1 = c(4L, 44L, 33L),
                      Root2 = c(5L, 11L, 2L), Root3 = c(6L, 33L, 222L),
                      Shoot1 = c(2L, 22L, 2222L), Shoot2 = c(4L, 44L, 2113L),
                      Shoot3 = c(5L, 33L, 2827L)), class = "data.frame", row.names = c(NA,
                                                                                           -3L))

# Split the dataframe into separate dataframes based on column names
sapply(split.default(df1, sub("\\d+$", "", names(df1))), 
       rowMeans, na.rm = TRUE)

# Exclude specific columns
sapply(split.default(df1, sub("\\d+$", "", names(df1))), function(x) 
    rowMeans(df1[setdiff(names(df1), names(x))], na.rm = TRUE))

sapply(unique(sub("\\d+$", "", names(df1))), \(nm)
   rowMeans(df1[grep(nm, names(df1), value = TRUE, invert = TRUE)], na.rm = TRUE))

In the first part of the code, we use split.default to split the dataframe into separate dataframes based on the column names that do not contain digits. Then we calculate the row means using sapply and rowMeans.

In the second part, we exclude specific columns by using setdiff to get the difference between all column names and the excluded group name, and then using these differences as indices for selecting rows in the dataframe.

Finally, we use grep to select only rows where the column name matches the excluded group name. We apply this filter to each group separately using sapply.

Using dplyr

Another way to solve this problem is by using the dplyr library’s row_means() function from the dplyr::rowsums() function, which can be used along with the group_by and select functions.

# Load necessary libraries
library(dplyr)

# Create a sample dataframe
df1 <- structure(list(Leaf1 = c(1L, 46L, 100L), Leaf2 = c(2L, 22L, 22L),
                      Leaf3 = c(3L, 33L, 2L), Root1 = c(4L, 44L, 33L),
                      Root2 = c(5L, 11L, 2L), Root3 = c(6L, 33L, 222L),
                      Shoot1 = c(2L, 22L, 2222L), Shoot2 = c(4L, 44L, 2113L),
                      Shoot3 = c(5L, 33L, 2827L)), class = "data.frame", row.names = c(NA,
                                                                                           -3L))

# Calculate row means excluding specific columns
df1 %>%
  group_by(excluded_group) %>%
  summarise(row_means = rowsums(df1[setdiff(names(df1), names(excluded_group))], 
                                na.rm = TRUE))

In this code, we use group_by to group the dataframe by an excluded group name. Then we calculate the row means using rowsums, excluding all columns that are in the excluded group.

Conclusion

Calculating the row mean of a dataframe excluding specific columns can be achieved using both base R and dplyr libraries. The choice between these two approaches depends on personal preference, familiarity with certain functions, or performance considerations.

Last modified on 2023-11-21